Three-level main-effects designs exploiting prior information about model uncertainty

To explore the projection efficiency of a design, Tsai, et al [2000. Projective three-level main effects designs robust to model uncertainty. Biometrika 87, 467–475] introduced the Q criterion to compare three-level main-effects designs for quantitative factors that allow the consideration of interactions in addition to main effects. In this paper, we extend their method and focus on the case in which experimenters have some prior knowledge, in advance of running the experiment, about the probabilities of effects being non-negligible. A criterion which incorporates experimenters’ prior beliefs about the importance of each effect is introduced to compare orthogonal, or nearly orthogonal, main effects designs with robustness to interactions as a secondary consideration. We show that this criterion, exploiting prior information about model uncertainty, can lead to more appropriate designs reflecting experimenters’ prior beliefs.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

Is the Easy Life Always the Happiest? Examining the Association of Convenience and Well-Being in Taiwan

Social scientists have under-examined neighborhood stores and other “resources” and their relationships to community welfare and personal happiness. Because the presence of neighborhood conveniences may signify that a neighborhood caters to residents’ needs and smoothes out the hassles of their daily lives, it could be hypothesized that commercial amenities and services enhance individuals’ satisfaction with their neighborhoods, with their health, and even with their lives as a whole. This study used a national probability sample from Taiwan, a densely populated society in East Asia, to test if service-oriented commercial and religious enterprises in neighborhoods are associated with positive estimations of well-being by those who occupy these spaces. We empirically examine whether proximity to main roads, night markets and temples or proximity to smoky food stands and other shops that produce pungent products affects well-being. Our findings from multivariate analyses suggest that if nearby conveniences are conceived as annoyances, they tend to lower satisfaction with neighborhood, but they do not lower life satisfaction in general. In contrast, air quality, along with “peace and quietness” is reported by respondents to be key in enhancing general well-being. We discuss the policy implications in the concluding session.     

A note on the non-negativity of continuous-time ARMA and GARCH processes

A general approach for modeling the volatility process in continuous-time is based on the convolution of a kernel with a non-decreasing Lévy process, which is non-negative if the kernel is non-negative. Within the framework of Continuous-time Auto-Regressive Moving-Average (CARMA) processes, we derive a necessary condition for the kernel to be non-negative, and propose a numerical method for checking the non-negativity of a kernel function. These results can be lifted to solving a similar problem with another approach to modeling volatility via the COntinuous-time Generalized Auto-Regressive Conditional Heteroscedastic (COGARCH) processes.     

Robust williams trent test

To investigate the biological activities of a new compound or drug, experimenters usually compare a series of increasing doses to a control. Among other objectives, one may try to investigate any possible dose-response trend and to determine the minimum effective dose among all the experimental doses. Williams (1971, 1972) proposed a procedure to test the dose-response trend and also to identify the minimum effective dose based on the normally distributed data. In this paper, we propose a similar test procedure based on the robust estimate'of the average response to perform similar analysis. The proposed method is more resistant to the outliers and more powerful than the Williams procedure when the data distribution deviates from normality. We illustrate the use of this procedure with data arising from a recent study.     

A correlation type procedure for assessing multivariate normality

A correlation-type statistic for assessing multivariate normality is described. Its estimated finite sample distribution is tabulated, and its performance against certain alternatives is compared with that of a competing Cramer-von Mises type statistic in a Monte Carlo power study. A set of quadrivariate data is examined as illustration of the procedure.     

Psychometric testing of the Chinese version of ISPCAN Child Abuse Screening Tools Children's Home Version (ICAST-CH-C)

BackgroundChild maltreatment is a global problem and the true extent remains unknown. The International Society for the Prevention of Child Abuse and Neglect (ISPCAN) Child Abuse Screening Tool — Children's Home version (ICAST-CH) has provided accurate assessment of the scope and prevalence of child maltreatment. Yet measures of children's experiences of child maltreatment are limited in the Chinese population.ObjectivesThe study aimed to translate and validate a Chinese version of the ISPCAN Child Abuse Screening Tool — Children's Home version (ICAST-CH) and to evaluate its reliability and validity among Taiwan adolescents.MethodsA three phase study was conducted. In phase 1, the ICAST was translated into Chinese using forward–backward translation procedures with the translation equivalence and content validity assessed. In phase 2, the data provided by a convenience sample of 98 adolescents was used to assess the internal consistency of the ICAST-CH Chinese version (ICAST-CH-C). In phase 3, the psychometric properties of the ICAST-CH-C were tested with a nationwide random sample of 5236 adolescents from 35 schools.ResultsThe translation equivalence and content validity index of the ICAST-CH-C was satisfactory. The inter-rater agreements were .90–.91 for comparability of language and .89–.94 for similarity of interpretability. Results indicated that the ICAST-CH-C had a high level of equivalence with the original English version and demonstrated a high internal consistency (.71–.89). Confirmatory factor analysis revealed the presence of five factors supporting the conceptual dimension of the original instrument.ConclusionThis study provided initial psychometric properties of the ICAST-CH-C and supports it as a reliable, valid, and highly usable instrument to identify childhood victimization in adolescents. It provided health care professionals with a useful tool to assess the severity and prevalence of child maltreatment within Chinese communities.     

Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion

Many different methods have been proposed to construct nonparametric estimates of a smooth regression function, including local polynomial, (convolution) kernel and smoothing spline estimators. Each of these estimators uses a smoothing parameter to control the amount of smoothing performed on a given data set. In this paper an improved version of a criterion based on the Akaike information criterion (AIC), termed AIC_C, is derived and examined as a way to choose the smoothing parameter. Unlike plug-in methods, AIC_C can be used to choose smoothing parameters for any linear smoother, including local quadratic and smoothing spline estimators. The use of AIC_C avoids the large variability and tendency to undersmooth (compared with the actual minimizer of average squared error) seen when other 'classical' approaches (such as generalized cross-validation (GCV) or the AIC) are used to choose the smoothing parameter. Monte Carlo simulations demonstrate that the AIC_C-based smoothing parameter is competitive with a plug-in method (assuming that one exists) when the plug-in method works well but also performs well when the plug-in approach fails or is unavailable.